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Featured in Development

Peter Alvaro talks about the reasons one should engage in language design and why many of us would (or should) do something so perverse as to design a language that no one will ever use. He shares some of the extreme and sometimes obnoxious opinions that guided his design process.

Featured in AI, ML & Data Engineering

Today on The InfoQ Podcast, Wes talks with Katharine Jarmul about privacy and fairness in machine learning algorithms. Jarul discusses what’s meant by Ethical Machine Learning and some things to consider when working towards achieving fairness. Jarmul is the co-founder at KIProtect a machine learning security and privacy firm based in Germany and is one of the three keynote speakers at QCon.ai.

Featured in Culture & Methods

Organizations struggle to scale their agility. While every organization is different, common patterns explain the major challenges that most organizations face: organizational design, trying to copy others, “one-size-fits-all” scaling, scaling in siloes, and neglecting engineering practices. This article explains why, what to do about it, and how the three leading scaling frameworks compare.

Double-loop learning in retrospectives and the Lean Startup

Sanjiv Augustine and Esther Derby highlight how the concept of double-loop learning can be a great model for encouraging transformational improvements in teams by challenging key assumptions and strategies.

On his website, Chris Argyris clarifies the difference between single-loop and double-loop learning.

Single-loop learning seems to be present when goals, values, frameworks and, to a significant extent, strategies are taken for granted. The emphasis is on ‘techniques and making techniques more efficient’. Any reflection is directed toward making the strategy more effective.

Double-loop learning, in contrast, involves questioning the role of the framing and learning systems which underlie actual goals and strategies

Sanjiv Augustine weighs in further on the importance of retrospectives and double-loop learning.

The retrospective represents a simple way to implement deeper double-loop learning beyond the day-to-day adjustments we make to keep things running on our projects. Retrospectives are thus fundamental to continuous improvement. Our teams and organizations cannot improve unless we're constantly assessing and improving our processes, and the retrospective is an excellent way to implement that continuous improvement.

A post on Mark [sweep]’s blog talks about how double-loop learning is also used in the Lean Startup framework.

The Lean Startup framework also has double-loop learning: It’s called a pivot. When you start off working with the Lean Startup framework you explicitly state your “leap-of-faith” assumptions. These are the baseline hypotheses you are testing with your startup’s initial strategy.

At a certain point in time, you come back and re-evaluated these “leap-of-faith” assumptions in what is called a pivot or persevere meeting. In this meeting, you decide whether to continue to optimize your current strategy, or pivot to a new strategy/assumptions based on your learning.

What techniques have you used to promote double-loop learning in your team?